engineering · 1 min read
Prompt Engineering Best Practices for Production
Lessons learned from deploying thousands of AI agents: how to write prompts that work reliably.
BABhogar AI TeamProduct & Engineering
After helping hundreds of teams deploy AI agents, we've distilled our prompt engineering knowledge into a set of battle-tested practices. These aren't theoretical tips - they come from real production deployments.
Structure is everything. Use clear sections in your system prompt: ROLE, CONTEXT, INSTRUCTIONS, CONSTRAINTS, OUTPUT FORMAT. This helps the LLM organize its reasoning and produces more consistent outputs across different inputs.
Be specific about what the agent should NOT do. Negative constraints ("Never discuss competitor products", "Do not make up information not in the provided context") are often more effective than positive instructions alone.
Test prompts systematically. Use BhogarAI's evaluation module to run your prompts against a test suite of representative inputs. Track metrics like relevance, accuracy, and safety scores across prompt iterations.
Version your prompts. Treat system prompts like code - use BhogarAI's prompt versioning to track changes, A/B test variants, and roll back if a new version degrades quality. In our experience, small prompt changes can have outsized effects on output quality.